Customer Shopping Trends Report

Overview

Brought to you by YData

Dataset statistics

Number of variables 19
Number of observations 3900
Missing cells 0
Missing cells (%) 0.0%
Duplicate rows 0
Duplicate rows (%) 0.0%
Total size in memory 3.4 MiB
Average record size in memory 921.6 B

Variable types

Numeric 5
Categorical 11
Boolean 3

Alerts

Category is highly overall correlated with Item_Purchased High correlation
Customer_ID is highly overall correlated with Discount_Applied and 3 other fields High correlation
Discount_Applied is highly overall correlated with Customer_ID and 3 other fields High correlation
Gender is highly overall correlated with Customer_ID and 2 other fields High correlation
Item_Purchased is highly overall correlated with Category High correlation
Promo_Code_Used is highly overall correlated with Customer_ID and 3 other fields High correlation
Subscription_Status is highly overall correlated with Customer_ID and 2 other fields High correlation
Customer_ID is uniformly distributed Uniform
Customer_ID has unique values Unique

Reproduction

Analysis started 2025-06-04 13:02:58.865779
Analysis finished 2025-06-04 13:03:04.430884
Duration 5.57 seconds
Software version ydata-profiling vv4.16.1
Download configuration config.json

Variables

Customer_ID
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct 3900
Distinct (%) 100.0%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 1950.5
Minimum 1
Maximum 3900
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 30.6 KiB
2025-06-04T13:03:04.548672 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 1
5-th percentile 195.95
Q1 975.75
median 1950.5
Q3 2925.25
95-th percentile 3705.05
Maximum 3900
Range 3899
Interquartile range (IQR) 1949.5

Descriptive statistics

Standard deviation 1125.9774
Coefficient of variation (CV) 0.57727626
Kurtosis -1.2
Mean 1950.5
Median Absolute Deviation (MAD) 975
Skewness 0
Sum 7606950
Variance 1267825
Monotonicity Strictly increasing
2025-06-04T13:03:04.691213 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
3900 1
 
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
Other values (3890) 3890
99.7%
Value Count Frequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
Value Count Frequency (%)
3900 1
< 0.1%
3899 1
< 0.1%
3898 1
< 0.1%
3897 1
< 0.1%
3896 1
< 0.1%
3895 1
< 0.1%
3894 1
< 0.1%
3893 1
< 0.1%
3892 1
< 0.1%
3891 1
< 0.1%

Age
Real number (ℝ)

Distinct 53
Distinct (%) 1.4%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 44.068462
Minimum 18
Maximum 70
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 30.6 KiB
2025-06-04T13:03:04.840769 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 18
5-th percentile 20
Q1 31
median 44
Q3 57
95-th percentile 68
Maximum 70
Range 52
Interquartile range (IQR) 26

Descriptive statistics

Standard deviation 15.207589
Coefficient of variation (CV) 0.34509008
Kurtosis -1.1950871
Mean 44.068462
Median Absolute Deviation (MAD) 13
Skewness -0.0063797217
Sum 171867
Variance 231.27077
Monotonicity Not monotonic
2025-06-04T13:03:04.980121 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
69 88
 
2.3%
57 87
 
2.2%
41 86
 
2.2%
25 85
 
2.2%
49 84
 
2.2%
54 83
 
2.1%
27 83
 
2.1%
50 83
 
2.1%
62 83
 
2.1%
32 82
 
2.1%
Other values (43) 3056
78.4%
Value Count Frequency (%)
18 69
1.8%
19 81
2.1%
20 62
1.6%
21 69
1.8%
22 66
1.7%
23 71
1.8%
24 68
1.7%
25 85
2.2%
26 69
1.8%
27 83
2.1%
Value Count Frequency (%)
70 67
1.7%
69 88
2.3%
68 75
1.9%
67 54
1.4%
66 71
1.8%
65 72
1.8%
64 73
1.9%
63 75
1.9%
62 83
2.1%
61 65
1.7%

Gender
Categorical

High correlation 

Distinct 2
Distinct (%) 0.1%
Missing 0
Missing (%) 0.0%
Memory size 234.9 KiB
Male
2652 
Female
1248 

Length

Max length 6
Median length 4
Mean length 4.64
Min length 4

Characters and Unicode

Total characters 18096
Distinct characters 6
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Male
2nd row Male
3rd row Male
4th row Male
5th row Male

Common Values

Value Count Frequency (%)
Male 2652
68.0%
Female 1248
32.0%

Length

2025-06-04T13:03:05.105656 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-04T13:03:05.237062 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
male 2652
68.0%
female 1248
32.0%

Most occurring characters

Value Count Frequency (%)
e 5148
28.4%
a 3900
21.6%
l 3900
21.6%
M 2652
14.7%
F 1248
 
6.9%
m 1248
 
6.9%

Most occurring categories

Value Count Frequency (%)
(unknown) 18096
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
e 5148
28.4%
a 3900
21.6%
l 3900
21.6%
M 2652
14.7%
F 1248
 
6.9%
m 1248
 
6.9%

Most occurring scripts

Value Count Frequency (%)
(unknown) 18096
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
e 5148
28.4%
a 3900
21.6%
l 3900
21.6%
M 2652
14.7%
F 1248
 
6.9%
m 1248
 
6.9%

Most occurring blocks

Value Count Frequency (%)
(unknown) 18096
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
e 5148
28.4%
a 3900
21.6%
l 3900
21.6%
M 2652
14.7%
F 1248
 
6.9%
m 1248
 
6.9%

Item_Purchased
Categorical

High correlation 

Distinct 25
Distinct (%) 0.6%
Missing 0
Missing (%) 0.0%
Memory size 239.6 KiB
Blouse
 
171
Pants
 
171
Jewelry
 
171
Shirt
 
169
Dress
 
166
Other values (20)
3052 

Length

Max length 10
Median length 8
Mean length 5.8746154
Min length 3

Characters and Unicode

Total characters 22911
Distinct characters 31
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Blouse
2nd row Sweater
3rd row Jeans
4th row Sandals
5th row Blouse

Common Values

Value Count Frequency (%)
Blouse 171
 
4.4%
Pants 171
 
4.4%
Jewelry 171
 
4.4%
Shirt 169
 
4.3%
Dress 166
 
4.3%
Sweater 164
 
4.2%
Jacket 163
 
4.2%
Coat 161
 
4.1%
Sunglasses 161
 
4.1%
Belt 161
 
4.1%
Other values (15) 2242
57.5%

Length

2025-06-04T13:03:05.392673 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
Value Count Frequency (%)
blouse 171
 
4.4%
pants 171
 
4.4%
jewelry 171
 
4.4%
shirt 169
 
4.3%
dress 166
 
4.3%
sweater 164
 
4.2%
jacket 163
 
4.2%
coat 161
 
4.1%
sunglasses 161
 
4.1%
belt 161
 
4.1%
Other values (15) 2242
57.5%

Most occurring characters

Value Count Frequency (%)
s 2483
 
10.8%
e 2347
 
10.2%
a 2312
 
10.1%
t 1749
 
7.6%
S 1580
 
6.9%
o 1528
 
6.7%
r 1434
 
6.3%
l 964
 
4.2%
n 914
 
4.0%
k 911
 
4.0%
Other values (21) 6689
29.2%

Most occurring categories

Value Count Frequency (%)
(unknown) 22911
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
s 2483
 
10.8%
e 2347
 
10.2%
a 2312
 
10.1%
t 1749
 
7.6%
S 1580
 
6.9%
o 1528
 
6.7%
r 1434
 
6.3%
l 964
 
4.2%
n 914
 
4.0%
k 911
 
4.0%
Other values (21) 6689
29.2%

Most occurring scripts

Value Count Frequency (%)
(unknown) 22911
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
s 2483
 
10.8%
e 2347
 
10.2%
a 2312
 
10.1%
t 1749
 
7.6%
S 1580
 
6.9%
o 1528
 
6.7%
r 1434
 
6.3%
l 964
 
4.2%
n 914
 
4.0%
k 911
 
4.0%
Other values (21) 6689
29.2%

Most occurring blocks

Value Count Frequency (%)
(unknown) 22911
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
s 2483
 
10.8%
e 2347
 
10.2%
a 2312
 
10.1%
t 1749
 
7.6%
S 1580
 
6.9%
o 1528
 
6.7%
r 1434
 
6.3%
l 964
 
4.2%
n 914
 
4.0%
k 911
 
4.0%
Other values (21) 6689
29.2%

Category
Categorical

High correlation 

Distinct 4
Distinct (%) 0.1%
Missing 0
Missing (%) 0.0%
Memory size 251.6 KiB
Clothing
1737 
Accessories
1240 
Footwear
599 
Outerwear
324 

Length

Max length 11
Median length 8
Mean length 9.0369231
Min length 8

Characters and Unicode

Total characters 35244
Distinct characters 18
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Clothing
2nd row Clothing
3rd row Clothing
4th row Footwear
5th row Clothing

Common Values

Value Count Frequency (%)
Clothing 1737
44.5%
Accessories 1240
31.8%
Footwear 599
 
15.4%
Outerwear 324
 
8.3%

Length

2025-06-04T13:03:05.561144 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-04T13:03:05.670833 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
clothing 1737
44.5%
accessories 1240
31.8%
footwear 599
 
15.4%
outerwear 324
 
8.3%

Most occurring characters

Value Count Frequency (%)
o 4175
11.8%
e 3727
10.6%
s 3720
10.6%
i 2977
 
8.4%
t 2660
 
7.5%
r 2487
 
7.1%
c 2480
 
7.0%
C 1737
 
4.9%
l 1737
 
4.9%
h 1737
 
4.9%
Other values (8) 7807
22.2%

Most occurring categories

Value Count Frequency (%)
(unknown) 35244
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
o 4175
11.8%
e 3727
10.6%
s 3720
10.6%
i 2977
 
8.4%
t 2660
 
7.5%
r 2487
 
7.1%
c 2480
 
7.0%
C 1737
 
4.9%
l 1737
 
4.9%
h 1737
 
4.9%
Other values (8) 7807
22.2%

Most occurring scripts

Value Count Frequency (%)
(unknown) 35244
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
o 4175
11.8%
e 3727
10.6%
s 3720
10.6%
i 2977
 
8.4%
t 2660
 
7.5%
r 2487
 
7.1%
c 2480
 
7.0%
C 1737
 
4.9%
l 1737
 
4.9%
h 1737
 
4.9%
Other values (8) 7807
22.2%

Most occurring blocks

Value Count Frequency (%)
(unknown) 35244
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
o 4175
11.8%
e 3727
10.6%
s 3720
10.6%
i 2977
 
8.4%
t 2660
 
7.5%
r 2487
 
7.1%
c 2480
 
7.0%
C 1737
 
4.9%
l 1737
 
4.9%
h 1737
 
4.9%
Other values (8) 7807
22.2%

Purchase_Amount_USD
Real number (ℝ)

Distinct 81
Distinct (%) 2.1%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 59.764359
Minimum 20
Maximum 100
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 30.6 KiB
2025-06-04T13:03:05.838630 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 20
5-th percentile 23
Q1 39
median 60
Q3 81
95-th percentile 96.05
Maximum 100
Range 80
Interquartile range (IQR) 42

Descriptive statistics

Standard deviation 23.685392
Coefficient of variation (CV) 0.396313
Kurtosis -1.2365937
Mean 59.764359
Median Absolute Deviation (MAD) 21
Skewness 0.012701758
Sum 233081
Variance 560.99781
Monotonicity Not monotonic
2025-06-04T13:03:06.039209 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
36 62
 
1.6%
32 62
 
1.6%
94 62
 
1.6%
51 61
 
1.6%
90 60
 
1.5%
68 59
 
1.5%
23 57
 
1.5%
25 56
 
1.4%
29 56
 
1.4%
88 55
 
1.4%
Other values (71) 3310
84.9%
Value Count Frequency (%)
20 52
1.3%
21 46
1.2%
22 44
1.1%
23 57
1.5%
24 50
1.3%
25 56
1.4%
26 51
1.3%
27 40
1.0%
28 51
1.3%
29 56
1.4%
Value Count Frequency (%)
100 36
0.9%
99 52
1.3%
98 52
1.3%
97 55
1.4%
96 53
1.4%
95 51
1.3%
94 62
1.6%
93 46
1.2%
92 38
1.0%
91 51
1.3%

Location
Categorical

Distinct 50
Distinct (%) 1.3%
Missing 0
Missing (%) 0.0%
Memory size 249.3 KiB
Montana
 
96
California
 
95
Idaho
 
93
Illinois
 
92
Alabama
 
89
Other values (45)
3435 

Length

Max length 14
Median length 12
Mean length 8.4284615
Min length 4

Characters and Unicode

Total characters 32871
Distinct characters 46
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Kentucky
2nd row Maine
3rd row Massachusetts
4th row Rhode Island
5th row Oregon

Common Values

Value Count Frequency (%)
Montana 96
 
2.5%
California 95
 
2.4%
Idaho 93
 
2.4%
Illinois 92
 
2.4%
Alabama 89
 
2.3%
Minnesota 88
 
2.3%
New York 87
 
2.2%
Nevada 87
 
2.2%
Nebraska 87
 
2.2%
Delaware 86
 
2.2%
Other values (40) 3000
76.9%

Length

2025-06-04T13:03:06.237493 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
Value Count Frequency (%)
new 306
 
6.6%
north 161
 
3.5%
virginia 158
 
3.4%
carolina 154
 
3.3%
dakota 153
 
3.3%
south 146
 
3.1%
montana 96
 
2.1%
california 95
 
2.0%
idaho 93
 
2.0%
illinois 92
 
2.0%
Other values (42) 3203
68.8%

Most occurring characters

Value Count Frequency (%)
a 4481
13.6%
i 3059
 
9.3%
n 2752
 
8.4%
o 2588
 
7.9%
s 2287
 
7.0%
e 2173
 
6.6%
r 1658
 
5.0%
t 1333
 
4.1%
l 1121
 
3.4%
h 975
 
3.0%
Other values (36) 10444
31.8%

Most occurring categories

Value Count Frequency (%)
(unknown) 32871
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
a 4481
13.6%
i 3059
 
9.3%
n 2752
 
8.4%
o 2588
 
7.9%
s 2287
 
7.0%
e 2173
 
6.6%
r 1658
 
5.0%
t 1333
 
4.1%
l 1121
 
3.4%
h 975
 
3.0%
Other values (36) 10444
31.8%

Most occurring scripts

Value Count Frequency (%)
(unknown) 32871
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
a 4481
13.6%
i 3059
 
9.3%
n 2752
 
8.4%
o 2588
 
7.9%
s 2287
 
7.0%
e 2173
 
6.6%
r 1658
 
5.0%
t 1333
 
4.1%
l 1121
 
3.4%
h 975
 
3.0%
Other values (36) 10444
31.8%

Most occurring blocks

Value Count Frequency (%)
(unknown) 32871
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
a 4481
13.6%
i 3059
 
9.3%
n 2752
 
8.4%
o 2588
 
7.9%
s 2287
 
7.0%
e 2173
 
6.6%
r 1658
 
5.0%
t 1333
 
4.1%
l 1121
 
3.4%
h 975
 
3.0%
Other values (36) 10444
31.8%

Size
Categorical

Distinct 4
Distinct (%) 0.1%
Missing 0
Missing (%) 0.0%
Memory size 221.4 KiB
M
1755 
L
1053 
S
663 
XL
429 

Length

Max length 2
Median length 1
Mean length 1.11
Min length 1

Characters and Unicode

Total characters 4329
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row L
2nd row L
3rd row S
4th row M
5th row M

Common Values

Value Count Frequency (%)
M 1755
45.0%
L 1053
27.0%
S 663
 
17.0%
XL 429
 
11.0%

Length

2025-06-04T13:03:06.665768 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-04T13:03:06.775579 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
m 1755
45.0%
l 1053
27.0%
s 663
 
17.0%
xl 429
 
11.0%

Most occurring characters

Value Count Frequency (%)
M 1755
40.5%
L 1482
34.2%
S 663
 
15.3%
X 429
 
9.9%

Most occurring categories

Value Count Frequency (%)
(unknown) 4329
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
M 1755
40.5%
L 1482
34.2%
S 663
 
15.3%
X 429
 
9.9%

Most occurring scripts

Value Count Frequency (%)
(unknown) 4329
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
M 1755
40.5%
L 1482
34.2%
S 663
 
15.3%
X 429
 
9.9%

Most occurring blocks

Value Count Frequency (%)
(unknown) 4329
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
M 1755
40.5%
L 1482
34.2%
S 663
 
15.3%
X 429
 
9.9%

Color
Categorical

Distinct 25
Distinct (%) 0.6%
Missing 0
Missing (%) 0.0%
Memory size 237.9 KiB
Olive
 
177
Yellow
 
174
Silver
 
173
Teal
 
172
Green
 
169
Other values (20)
3035 

Length

Max length 9
Median length 8
Mean length 5.4284615
Min length 3

Characters and Unicode

Total characters 21171
Distinct characters 34
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Gray
2nd row Maroon
3rd row Maroon
4th row Maroon
5th row Turquoise

Common Values

Value Count Frequency (%)
Olive 177
 
4.5%
Yellow 174
 
4.5%
Silver 173
 
4.4%
Teal 172
 
4.4%
Green 169
 
4.3%
Black 167
 
4.3%
Cyan 166
 
4.3%
Violet 166
 
4.3%
Gray 159
 
4.1%
Maroon 158
 
4.1%
Other values (15) 2219
56.9%

Length

2025-06-04T13:03:06.938127 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
Value Count Frequency (%)
olive 177
 
4.5%
yellow 174
 
4.5%
silver 173
 
4.4%
teal 172
 
4.4%
green 169
 
4.3%
black 167
 
4.3%
cyan 166
 
4.3%
violet 166
 
4.3%
gray 159
 
4.1%
maroon 158
 
4.1%
Other values (15) 2219
56.9%

Most occurring characters

Value Count Frequency (%)
e 2981
14.1%
a 1882
 
8.9%
l 1797
 
8.5%
r 1550
 
7.3%
n 1387
 
6.6%
o 1380
 
6.5%
i 1250
 
5.9%
B 607
 
2.9%
g 600
 
2.8%
u 593
 
2.8%
Other values (24) 7144
33.7%

Most occurring categories

Value Count Frequency (%)
(unknown) 21171
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
e 2981
14.1%
a 1882
 
8.9%
l 1797
 
8.5%
r 1550
 
7.3%
n 1387
 
6.6%
o 1380
 
6.5%
i 1250
 
5.9%
B 607
 
2.9%
g 600
 
2.8%
u 593
 
2.8%
Other values (24) 7144
33.7%

Most occurring scripts

Value Count Frequency (%)
(unknown) 21171
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
e 2981
14.1%
a 1882
 
8.9%
l 1797
 
8.5%
r 1550
 
7.3%
n 1387
 
6.6%
o 1380
 
6.5%
i 1250
 
5.9%
B 607
 
2.9%
g 600
 
2.8%
u 593
 
2.8%
Other values (24) 7144
33.7%

Most occurring blocks

Value Count Frequency (%)
(unknown) 21171
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
e 2981
14.1%
a 1882
 
8.9%
l 1797
 
8.5%
r 1550
 
7.3%
n 1387
 
6.6%
o 1380
 
6.5%
i 1250
 
5.9%
B 607
 
2.9%
g 600
 
2.8%
u 593
 
2.8%
Other values (24) 7144
33.7%

Season
Categorical

Distinct 4
Distinct (%) 0.1%
Missing 0
Missing (%) 0.0%
Memory size 238.2 KiB
Spring
999 
Fall
975 
Winter
971 
Summer
955 

Length

Max length 6
Median length 6
Mean length 5.5
Min length 4

Characters and Unicode

Total characters 21450
Distinct characters 14
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Winter
2nd row Winter
3rd row Spring
4th row Spring
5th row Spring

Common Values

Value Count Frequency (%)
Spring 999
25.6%
Fall 975
25.0%
Winter 971
24.9%
Summer 955
24.5%

Length

2025-06-04T13:03:07.109636 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-04T13:03:07.238925 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
spring 999
25.6%
fall 975
25.0%
winter 971
24.9%
summer 955
24.5%

Most occurring characters

Value Count Frequency (%)
r 2925
13.6%
i 1970
9.2%
n 1970
9.2%
S 1954
9.1%
l 1950
9.1%
e 1926
9.0%
m 1910
8.9%
p 999
 
4.7%
g 999
 
4.7%
F 975
 
4.5%
Other values (4) 3872
18.1%

Most occurring categories

Value Count Frequency (%)
(unknown) 21450
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
r 2925
13.6%
i 1970
9.2%
n 1970
9.2%
S 1954
9.1%
l 1950
9.1%
e 1926
9.0%
m 1910
8.9%
p 999
 
4.7%
g 999
 
4.7%
F 975
 
4.5%
Other values (4) 3872
18.1%

Most occurring scripts

Value Count Frequency (%)
(unknown) 21450
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
r 2925
13.6%
i 1970
9.2%
n 1970
9.2%
S 1954
9.1%
l 1950
9.1%
e 1926
9.0%
m 1910
8.9%
p 999
 
4.7%
g 999
 
4.7%
F 975
 
4.5%
Other values (4) 3872
18.1%

Most occurring blocks

Value Count Frequency (%)
(unknown) 21450
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
r 2925
13.6%
i 1970
9.2%
n 1970
9.2%
S 1954
9.1%
l 1950
9.1%
e 1926
9.0%
m 1910
8.9%
p 999
 
4.7%
g 999
 
4.7%
F 975
 
4.5%
Other values (4) 3872
18.1%

Review_Rating
Real number (ℝ)

Distinct 26
Distinct (%) 0.7%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 3.7499487
Minimum 2.5
Maximum 5
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 30.6 KiB
2025-06-04T13:03:07.430196 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 2.5
5-th percentile 2.6
Q1 3.1
median 3.7
Q3 4.4
95-th percentile 4.9
Maximum 5
Range 2.5
Interquartile range (IQR) 1.3

Descriptive statistics

Standard deviation 0.71622281
Coefficient of variation (CV) 0.19099536
Kurtosis -1.1796283
Mean 3.7499487
Median Absolute Deviation (MAD) 0.6
Skewness 0.0045245964
Sum 14624.8
Variance 0.51297512
Monotonicity Not monotonic
2025-06-04T13:03:07.616197 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
Value Count Frequency (%)
3.4 182
 
4.7%
4 181
 
4.6%
4.6 174
 
4.5%
4.2 171
 
4.4%
2.9 170
 
4.4%
4.9 166
 
4.3%
3.9 163
 
4.2%
3 162
 
4.2%
2.6 159
 
4.1%
4.4 158
 
4.1%
Other values (16) 2214
56.8%
Value Count Frequency (%)
2.5 66
 
1.7%
2.6 159
4.1%
2.7 154
3.9%
2.8 136
3.5%
2.9 170
4.4%
3 162
4.2%
3.1 157
4.0%
3.2 152
3.9%
3.3 152
3.9%
3.4 182
4.7%
Value Count Frequency (%)
5 68
 
1.7%
4.9 166
4.3%
4.8 144
3.7%
4.7 148
3.8%
4.6 174
4.5%
4.5 139
3.6%
4.4 158
4.1%
4.3 147
3.8%
4.2 171
4.4%
4.1 148
3.8%

Subscription_Status
Boolean

High correlation 

Distinct 2
Distinct (%) 0.1%
Missing 0
Missing (%) 0.0%
Memory size 3.9 KiB
False
2847 
True
1053 
Value Count Frequency (%)
False 2847
73.0%
True 1053
 
27.0%
2025-06-04T13:03:07.746128 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Payment_Method
Categorical

Distinct 6
Distinct (%) 0.2%
Missing 0
Missing (%) 0.0%
Memory size 248.4 KiB
Credit Card
696 
Venmo
653 
Cash
648 
PayPal
638 
Debit Card
633 

Length

Max length 13
Median length 10
Mean length 8.1761538
Min length 4

Characters and Unicode

Total characters 31887
Distinct characters 23
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Credit Card
2nd row Bank Transfer
3rd row Cash
4th row PayPal
5th row Cash

Common Values

Value Count Frequency (%)
Credit Card 696
17.8%
Venmo 653
16.7%
Cash 648
16.6%
PayPal 638
16.4%
Debit Card 633
16.2%
Bank Transfer 632
16.2%

Length

2025-06-04T13:03:07.843291 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-04T13:03:07.930983 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
card 1329
22.7%
credit 696
11.9%
venmo 653
11.1%
cash 648
11.1%
paypal 638
10.9%
debit 633
10.8%
bank 632
10.8%
transfer 632
10.8%

Most occurring characters

Value Count Frequency (%)
a 4517
14.2%
r 3289
 
10.3%
C 2673
 
8.4%
e 2614
 
8.2%
d 2025
 
6.4%
1961
 
6.1%
n 1917
 
6.0%
t 1329
 
4.2%
i 1329
 
4.2%
s 1280
 
4.0%
Other values (13) 8953
28.1%

Most occurring categories

Value Count Frequency (%)
(unknown) 31887
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
a 4517
14.2%
r 3289
 
10.3%
C 2673
 
8.4%
e 2614
 
8.2%
d 2025
 
6.4%
1961
 
6.1%
n 1917
 
6.0%
t 1329
 
4.2%
i 1329
 
4.2%
s 1280
 
4.0%
Other values (13) 8953
28.1%

Most occurring scripts

Value Count Frequency (%)
(unknown) 31887
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
a 4517
14.2%
r 3289
 
10.3%
C 2673
 
8.4%
e 2614
 
8.2%
d 2025
 
6.4%
1961
 
6.1%
n 1917
 
6.0%
t 1329
 
4.2%
i 1329
 
4.2%
s 1280
 
4.0%
Other values (13) 8953
28.1%

Most occurring blocks

Value Count Frequency (%)
(unknown) 31887
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
a 4517
14.2%
r 3289
 
10.3%
C 2673
 
8.4%
e 2614
 
8.2%
d 2025
 
6.4%
1961
 
6.1%
n 1917
 
6.0%
t 1329
 
4.2%
i 1329
 
4.2%
s 1280
 
4.0%
Other values (13) 8953
28.1%

Shipping_Type
Categorical

Distinct 6
Distinct (%) 0.2%
Missing 0
Missing (%) 0.0%
Memory size 259.1 KiB
Free Shipping
675 
Standard
654 
Store Pickup
650 
Next Day Air
648 
Express
646 

Length

Max length 14
Median length 13
Mean length 10.995641
Min length 7

Characters and Unicode

Total characters 42883
Distinct characters 27
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Express
2nd row Express
3rd row Free Shipping
4th row Next Day Air
5th row Free Shipping

Common Values

Value Count Frequency (%)
Free Shipping 675
17.3%
Standard 654
16.8%
Store Pickup 650
16.7%
Next Day Air 648
16.6%
Express 646
16.6%
2-Day Shipping 627
16.1%

Length

2025-06-04T13:03:08.056479 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-04T13:03:08.147476 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
shipping 1302
18.2%
free 675
9.4%
standard 654
9.1%
store 650
9.1%
pickup 650
9.1%
next 648
9.1%
day 648
9.1%
air 648
9.1%
express 646
9.0%
2-day 627
8.8%

Most occurring characters

Value Count Frequency (%)
i 3902
 
9.1%
p 3900
 
9.1%
e 3294
 
7.7%
r 3273
 
7.6%
3248
 
7.6%
S 2606
 
6.1%
a 2583
 
6.0%
n 1956
 
4.6%
t 1952
 
4.6%
d 1308
 
3.1%
Other values (17) 14861
34.7%

Most occurring categories

Value Count Frequency (%)
(unknown) 42883
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
i 3902
 
9.1%
p 3900
 
9.1%
e 3294
 
7.7%
r 3273
 
7.6%
3248
 
7.6%
S 2606
 
6.1%
a 2583
 
6.0%
n 1956
 
4.6%
t 1952
 
4.6%
d 1308
 
3.1%
Other values (17) 14861
34.7%

Most occurring scripts

Value Count Frequency (%)
(unknown) 42883
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
i 3902
 
9.1%
p 3900
 
9.1%
e 3294
 
7.7%
r 3273
 
7.6%
3248
 
7.6%
S 2606
 
6.1%
a 2583
 
6.0%
n 1956
 
4.6%
t 1952
 
4.6%
d 1308
 
3.1%
Other values (17) 14861
34.7%

Most occurring blocks

Value Count Frequency (%)
(unknown) 42883
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
i 3902
 
9.1%
p 3900
 
9.1%
e 3294
 
7.7%
r 3273
 
7.6%
3248
 
7.6%
S 2606
 
6.1%
a 2583
 
6.0%
n 1956
 
4.6%
t 1952
 
4.6%
d 1308
 
3.1%
Other values (17) 14861
34.7%

Discount_Applied
Boolean

High correlation 

Distinct 2
Distinct (%) 0.1%
Missing 0
Missing (%) 0.0%
Memory size 3.9 KiB
False
2223 
True
1677 
Value Count Frequency (%)
False 2223
57.0%
True 1677
43.0%
2025-06-04T13:03:08.249015 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Promo_Code_Used
Boolean

High correlation 

Distinct 2
Distinct (%) 0.1%
Missing 0
Missing (%) 0.0%
Memory size 3.9 KiB
False
2223 
True
1677 
Value Count Frequency (%)
False 2223
57.0%
True 1677
43.0%
2025-06-04T13:03:08.298524 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Previous_Purchases
Real number (ℝ)

Distinct 50
Distinct (%) 1.3%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 25.351538
Minimum 1
Maximum 50
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 30.6 KiB
2025-06-04T13:03:08.397692 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 1
5-th percentile 3
Q1 13
median 25
Q3 38
95-th percentile 48
Maximum 50
Range 49
Interquartile range (IQR) 25

Descriptive statistics

Standard deviation 14.447125
Coefficient of variation (CV) 0.56987173
Kurtosis -1.1901874
Mean 25.351538
Median Absolute Deviation (MAD) 12
Skewness 0.0031211555
Sum 98871
Variance 208.71943
Monotonicity Not monotonic
2025-06-04T13:03:08.556868 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
31 97
 
2.5%
21 96
 
2.5%
14 92
 
2.4%
4 91
 
2.3%
3 91
 
2.3%
24 91
 
2.3%
48 90
 
2.3%
47 90
 
2.3%
5 87
 
2.2%
6 87
 
2.2%
Other values (40) 2988
76.6%
Value Count Frequency (%)
1 83
2.1%
2 72
1.8%
3 91
2.3%
4 91
2.3%
5 87
2.2%
6 87
2.2%
7 65
1.7%
8 67
1.7%
9 65
1.7%
10 76
1.9%
Value Count Frequency (%)
50 77
2.0%
49 58
1.5%
48 90
2.3%
47 90
2.3%
46 78
2.0%
45 83
2.1%
44 72
1.8%
43 64
1.6%
42 83
2.1%
41 70
1.8%

Preferred_Payment_Method
Categorical

Distinct 6
Distinct (%) 0.2%
Missing 0
Missing (%) 0.0%
Memory size 248.1 KiB
PayPal
677 
Credit Card
671 
Cash
670 
Debit Card
636 
Venmo
634 

Length

Max length 13
Median length 10
Mean length 8.1048718
Min length 4

Characters and Unicode

Total characters 31609
Distinct characters 23
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Venmo
2nd row Cash
3rd row Credit Card
4th row PayPal
5th row PayPal

Common Values

Value Count Frequency (%)
PayPal 677
17.4%
Credit Card 671
17.2%
Cash 670
17.2%
Debit Card 636
16.3%
Venmo 634
16.3%
Bank Transfer 612
15.7%

Length

2025-06-04T13:03:08.679939 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-04T13:03:08.803950 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
card 1307
22.5%
paypal 677
11.6%
credit 671
11.5%
cash 670
11.5%
debit 636
10.9%
venmo 634
10.9%
bank 612
10.5%
transfer 612
10.5%

Most occurring characters

Value Count Frequency (%)
a 4555
14.4%
r 3202
 
10.1%
C 2648
 
8.4%
e 2553
 
8.1%
d 1978
 
6.3%
1919
 
6.1%
n 1858
 
5.9%
P 1354
 
4.3%
i 1307
 
4.1%
t 1307
 
4.1%
Other values (13) 8928
28.2%

Most occurring categories

Value Count Frequency (%)
(unknown) 31609
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
a 4555
14.4%
r 3202
 
10.1%
C 2648
 
8.4%
e 2553
 
8.1%
d 1978
 
6.3%
1919
 
6.1%
n 1858
 
5.9%
P 1354
 
4.3%
i 1307
 
4.1%
t 1307
 
4.1%
Other values (13) 8928
28.2%

Most occurring scripts

Value Count Frequency (%)
(unknown) 31609
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
a 4555
14.4%
r 3202
 
10.1%
C 2648
 
8.4%
e 2553
 
8.1%
d 1978
 
6.3%
1919
 
6.1%
n 1858
 
5.9%
P 1354
 
4.3%
i 1307
 
4.1%
t 1307
 
4.1%
Other values (13) 8928
28.2%

Most occurring blocks

Value Count Frequency (%)
(unknown) 31609
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
a 4555
14.4%
r 3202
 
10.1%
C 2648
 
8.4%
e 2553
 
8.1%
d 1978
 
6.3%
1919
 
6.1%
n 1858
 
5.9%
P 1354
 
4.3%
i 1307
 
4.1%
t 1307
 
4.1%
Other values (13) 8928
28.2%

Frequency_of_Purchases
Categorical

Distinct 7
Distinct (%) 0.2%
Missing 0
Missing (%) 0.0%
Memory size 252.2 KiB
Every 3 Months
584 
Annually
572 
Quarterly
563 
Monthly
553 
Bi-Weekly
547 
Other values (2)
1081 

Length

Max length 14
Median length 9
Mean length 9.1817949
Min length 6

Characters and Unicode

Total characters 35809
Distinct characters 25
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Fortnightly
2nd row Fortnightly
3rd row Weekly
4th row Weekly
5th row Annually

Common Values

Value Count Frequency (%)
Every 3 Months 584
15.0%
Annually 572
14.7%
Quarterly 563
14.4%
Monthly 553
14.2%
Bi-Weekly 547
14.0%
Fortnightly 542
13.9%
Weekly 539
13.8%

Length

2025-06-04T13:03:09.000383 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-04T13:03:09.159341 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
every 584
11.5%
3 584
11.5%
months 584
11.5%
annually 572
11.3%
quarterly 563
11.1%
monthly 553
10.9%
bi-weekly 547
10.8%
fortnightly 542
10.7%
weekly 539
10.6%

Most occurring characters

Value Count Frequency (%)
y 3900
 
10.9%
l 3888
 
10.9%
e 3319
 
9.3%
n 2823
 
7.9%
t 2784
 
7.8%
r 2252
 
6.3%
h 1679
 
4.7%
o 1679
 
4.7%
1168
 
3.3%
M 1137
 
3.2%
Other values (15) 11180
31.2%

Most occurring categories

Value Count Frequency (%)
(unknown) 35809
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
y 3900
 
10.9%
l 3888
 
10.9%
e 3319
 
9.3%
n 2823
 
7.9%
t 2784
 
7.8%
r 2252
 
6.3%
h 1679
 
4.7%
o 1679
 
4.7%
1168
 
3.3%
M 1137
 
3.2%
Other values (15) 11180
31.2%

Most occurring scripts

Value Count Frequency (%)
(unknown) 35809
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
y 3900
 
10.9%
l 3888
 
10.9%
e 3319
 
9.3%
n 2823
 
7.9%
t 2784
 
7.8%
r 2252
 
6.3%
h 1679
 
4.7%
o 1679
 
4.7%
1168
 
3.3%
M 1137
 
3.2%
Other values (15) 11180
31.2%

Most occurring blocks

Value Count Frequency (%)
(unknown) 35809
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
y 3900
 
10.9%
l 3888
 
10.9%
e 3319
 
9.3%
n 2823
 
7.9%
t 2784
 
7.8%
r 2252
 
6.3%
h 1679
 
4.7%
o 1679
 
4.7%
1168
 
3.3%
M 1137
 
3.2%
Other values (15) 11180
31.2%

Interactions

2025-06-04T13:03:03.461035 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-04T13:03:00.901514 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-04T13:03:01.524874 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-04T13:03:02.074974 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-04T13:03:02.709154 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-04T13:03:03.589303 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-04T13:03:01.019938 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-04T13:03:01.644154 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-04T13:03:02.225509 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-04T13:03:02.996967 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-04T13:03:03.691450 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-04T13:03:01.159778 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-04T13:03:01.747118 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-04T13:03:02.341954 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-04T13:03:03.103583 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-04T13:03:03.813184 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-04T13:03:01.288508 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-04T13:03:01.859286 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-04T13:03:02.467583 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-04T13:03:03.234541 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-04T13:03:03.918395 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-04T13:03:01.400758 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-04T13:03:01.966270 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-04T13:03:02.587664 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-04T13:03:03.354697 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-04T13:03:09.357145 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Age Category Color Customer_ID Discount_Applied Frequency_of_Purchases Gender Item_Purchased Location Payment_Method Preferred_Payment_Method Previous_Purchases Promo_Code_Used Purchase_Amount_USD Review_Rating Season Shipping_Type Size Subscription_Status
Age 1.000 0.000 0.000 -0.004 0.000 0.000 0.000 0.032 0.051 0.000 0.024 0.041 0.000 -0.010 -0.022 0.010 0.028 0.029 0.000
Category 0.000 1.000 0.005 0.000 0.000 0.000 0.000 0.997 0.000 0.017 0.000 0.000 0.000 0.000 0.025 0.000 0.000 0.000 0.000
Color 0.000 0.005 1.000 0.000 0.000 0.000 0.000 0.009 0.000 0.000 0.024 0.025 0.000 0.000 0.028 0.000 0.016 0.000 0.000
Customer_ID -0.004 0.000 0.000 1.000 0.955 0.012 0.961 0.021 0.023 0.000 0.000 -0.039 0.955 0.011 0.001 0.000 0.000 0.020 0.944
Discount_Applied 0.000 0.000 0.000 0.955 1.000 0.000 0.595 0.045 0.000 0.000 0.000 0.028 0.999 0.000 0.000 0.007 0.000 0.000 0.700
Frequency_of_Purchases 0.000 0.000 0.000 0.012 0.000 1.000 0.000 0.000 0.000 0.018 0.015 0.005 0.000 0.021 0.000 0.000 0.008 0.000 0.000
Gender 0.000 0.000 0.000 0.961 0.595 0.000 1.000 0.008 0.000 0.000 0.000 0.050 0.595 0.000 0.000 0.000 0.043 0.030 0.416
Item_Purchased 0.032 0.997 0.009 0.021 0.045 0.000 0.008 1.000 0.000 0.034 0.000 0.030 0.045 0.000 0.027 0.023 0.000 0.000 0.013
Location 0.051 0.000 0.000 0.023 0.000 0.000 0.000 0.000 1.000 0.000 0.029 0.032 0.000 0.012 0.000 0.032 0.000 0.000 0.000
Payment_Method 0.000 0.017 0.000 0.000 0.000 0.018 0.000 0.034 0.000 1.000 0.004 0.030 0.000 0.014 0.033 0.000 0.000 0.000 0.000
Preferred_Payment_Method 0.024 0.000 0.024 0.000 0.000 0.015 0.000 0.000 0.029 0.004 1.000 0.018 0.000 0.000 0.015 0.025 0.000 0.020 0.000
Previous_Purchases 0.041 0.000 0.025 -0.039 0.028 0.005 0.050 0.030 0.032 0.030 0.018 1.000 0.028 0.008 0.004 0.000 0.017 0.000 0.013
Promo_Code_Used 0.000 0.000 0.000 0.955 0.999 0.000 0.595 0.045 0.000 0.000 0.000 0.028 1.000 0.000 0.000 0.007 0.000 0.000 0.700
Purchase_Amount_USD -0.010 0.000 0.000 0.011 0.000 0.021 0.000 0.000 0.012 0.014 0.000 0.008 0.000 1.000 0.030 0.034 0.005 0.021 0.000
Review_Rating -0.022 0.025 0.028 0.001 0.000 0.000 0.000 0.027 0.000 0.033 0.015 0.004 0.000 0.030 1.000 0.000 0.027 0.036 0.000
Season 0.010 0.000 0.000 0.000 0.007 0.000 0.000 0.023 0.032 0.000 0.025 0.000 0.007 0.034 0.000 1.000 0.000 0.000 0.000
Shipping_Type 0.028 0.000 0.016 0.000 0.000 0.008 0.043 0.000 0.000 0.000 0.000 0.017 0.000 0.005 0.027 0.000 1.000 0.000 0.018
Size 0.029 0.000 0.000 0.020 0.000 0.000 0.030 0.000 0.000 0.000 0.020 0.000 0.000 0.021 0.036 0.000 0.000 1.000 0.000
Subscription_Status 0.000 0.000 0.000 0.944 0.700 0.000 0.416 0.013 0.000 0.000 0.000 0.013 0.700 0.000 0.000 0.000 0.018 0.000 1.000

Missing values

2025-06-04T13:03:04.098130 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-04T13:03:04.311279 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Customer_ID Age Gender Item_Purchased Category Purchase_Amount_USD Location Size Color Season Review_Rating Subscription_Status Payment_Method Shipping_Type Discount_Applied Promo_Code_Used Previous_Purchases Preferred_Payment_Method Frequency_of_Purchases
0 1 55 Male Blouse Clothing 53 Kentucky L Gray Winter 3.1 Yes Credit Card Express Yes Yes 14 Venmo Fortnightly
1 2 19 Male Sweater Clothing 64 Maine L Maroon Winter 3.1 Yes Bank Transfer Express Yes Yes 2 Cash Fortnightly
2 3 50 Male Jeans Clothing 73 Massachusetts S Maroon Spring 3.1 Yes Cash Free Shipping Yes Yes 23 Credit Card Weekly
3 4 21 Male Sandals Footwear 90 Rhode Island M Maroon Spring 3.5 Yes PayPal Next Day Air Yes Yes 49 PayPal Weekly
4 5 45 Male Blouse Clothing 49 Oregon M Turquoise Spring 2.7 Yes Cash Free Shipping Yes Yes 31 PayPal Annually
5 6 46 Male Sneakers Footwear 20 Wyoming M White Summer 2.9 Yes Venmo Standard Yes Yes 14 Venmo Weekly
6 7 63 Male Shirt Clothing 85 Montana M Gray Fall 3.2 Yes Debit Card Free Shipping Yes Yes 49 Cash Quarterly
7 8 27 Male Shorts Clothing 34 Louisiana L Charcoal Winter 3.2 Yes Debit Card Free Shipping Yes Yes 19 Credit Card Weekly
8 9 26 Male Coat Outerwear 97 West Virginia L Silver Summer 2.6 Yes Venmo Express Yes Yes 8 Venmo Annually
9 10 57 Male Handbag Accessories 31 Missouri M Pink Spring 4.8 Yes PayPal 2-Day Shipping Yes Yes 4 Cash Quarterly
Customer_ID Age Gender Item_Purchased Category Purchase_Amount_USD Location Size Color Season Review_Rating Subscription_Status Payment_Method Shipping_Type Discount_Applied Promo_Code_Used Previous_Purchases Preferred_Payment_Method Frequency_of_Purchases
3890 3891 35 Female Shirt Clothing 81 Nebraska XL Green Winter 2.6 No Credit Card Standard No No 33 Debit Card Annually
3891 3892 36 Female Dress Clothing 30 Colorado L Peach Winter 4.7 No Cash Free Shipping No No 6 Bank Transfer Quarterly
3892 3893 35 Female Jewelry Accessories 86 Michigan L Indigo Summer 3.5 No Bank Transfer Standard No No 5 PayPal Fortnightly
3893 3894 21 Female Hat Accessories 64 Massachusetts L White Fall 3.3 No Bank Transfer Store Pickup No No 29 Bank Transfer Bi-Weekly
3894 3895 66 Female Skirt Clothing 78 Connecticut L White Spring 3.9 No Cash 2-Day Shipping No No 44 Credit Card Every 3 Months
3895 3896 40 Female Hoodie Clothing 28 Virginia L Turquoise Summer 4.2 No Cash 2-Day Shipping No No 32 Venmo Weekly
3896 3897 52 Female Backpack Accessories 49 Iowa L White Spring 4.5 No PayPal Store Pickup No No 41 Bank Transfer Bi-Weekly
3897 3898 46 Female Belt Accessories 33 New Jersey L Green Spring 2.9 No Credit Card Standard No No 24 Venmo Quarterly
3898 3899 44 Female Shoes Footwear 77 Minnesota S Brown Summer 3.8 No PayPal Express No No 24 Venmo Weekly
3899 3900 52 Female Handbag Accessories 81 California M Beige Spring 3.1 No Bank Transfer Store Pickup No No 33 Venmo Quarterly